MachineLearningSystemsEngineer

Architect, build, and operate scalable backend services for a media intelligence platform, focusing on clean, maintainable, and production-ready systems.

Own critical backend components end-to-end, from system design and API contracts through implementation, deployment, monitoring, and iteration.

Drive architectural decisions across APIs, processing pipelines, distributed compute, storage, search, observability, cloud infrastructure, and model-serving workflows.

Design data models and storage patterns for media assets, generated metadata, embeddings, processing jobs, model outputs, search indexes, and audit trails.

Design high-throughput media ingestion and processing pipelines for large volumes of video, audio, image, and text content.

Build distributed, event-driven workflows for media processing using queues and pub/sub systems.

Implement reliable asynchronous processing patterns, including retries, idempotency, dead-letter queues, backpressure handling, and fault-tolerant job execution.

Lead the development and optimization of metadata extraction, content analysis, scene detection, transcription, embedding generation, and multimodal AI inference workflows.

Integrate and optimize AI/ML services within backend workflows, including model APIs, embedding pipelines, OCR, speech-to-text, scene analysis, multimodal inference, batching, caching, and fallback strategies.

Collaborate with ML engineers, data scientists, or external model providers to benchmark models, compare quality/latency trade-offs, and safely roll out model upgrades.

Optimize AI/ML inference workflows for latency, throughput, reliability, and cost across both real-time and batch-processing paths.

Work with model-serving systems to improve batching, concurrency, warmup behavior, timeout handling, autoscaling, and GPU utilization.

Evaluate and apply practical model optimization techniques such as quantization, model distillation, batching, caching, prompt optimization, and routing to smaller or cheaper models where appropriate.

Design and maintain vector search and indexing systems.

Build retrieval workflows that support semantic search, similarity matching, duplicate detection, media discovery, and structured metadata search.

Monitor model and system performance in production, including API latency, queue depth, processing time, model error rates, GPU utilization, confidence distributions, drift signals, and cost per processed item.

Deploy and operate systems on AWS, GCP, Azure, or equivalent cloud platforms, including compute, storage, networking, queues, model-serving infrastructure, and monitoring systems.

Ensure system reliability through logging, metrics, tracing, alerting, dashboards, operational runbooks, and incident-response best practices.